Performance Prediction of Roadheader by PSO-SVR Algorithm
Research purposes:The roadheader works as an indispensable mechanical equipment in the milling and excavation method.Its working performance is often constrained by the characteristics of surrounding rock and mechanical equipment conditions.To predict the working performance of roadheader,this paper takes the instantaneous cutting rate(ICR)as the evaluation index,comprehensively considers the surrounding rock factors and mechanical equipment factors,and establishes an ICR prediction system based on different algorithms.The support vector machine algorithm based on particle swarm optimization(PSO-SVR)is optimized,which supported the ICR prediction system.This system can efficiently predict the working performance and excavating speed of tunnels excavated by roadheader.Research conclusions:(1)The data of tunnels excavated by roadheader are used for the training samples of 5 different algorithms.The PSO-SVR algorithm has the best prediction accuracy.(2)The particle swarm optimization is used to search the optimal penalty coefficient C and kernel function coefficient g,which can effectively avoid falling into local optimal solution and significantly improve the prediction accuracy and generalization ability of the model.(3)The ten-fold cross validation results indicate that the PSO-SVR model has better robustness than that of the other four algorithms.(4)Based on the Ganzhou Rongjiang Tunnel,the ICR and excavating speed of the roadheader is accurately predicted by PSO-SVR model,whose prediction accuracy is significantly higher than the empirical formula and other four algorithms,providing reference for the selection of roadheader and the prediction of excavating speed of roadheader.
万方数据
维普
